This extensive research delves into the intersection of MRI imaging and deep learning, in the task of identifying and categorizing brain tumors. In addition to models like VGG16 and ResNet101 a designed Convolutional Neural Network (CNN) was developed and thoroughly evaluated showcasing a range of techniques utilized. Augmentation methods were purposefully applied to enrich the dataset, enhancing the models’ robustness and adaptability. Evaluation metrics, including the F1 score, recall, accuracy, and precision gave a general picture of the model’s performance. Furthermore, leveraging Explainable AI (XAI) techniques such as LIME unveiled insights, into the decision-making processes underlying the models enhancing their interpretability and trustworthiness. The study findings ultimately underscore the potential of learning in revolutionizing automated brain tumor diagnosis and classification poised to enhance patient care pathways and medical diagnostic capabilities significantly.